Podcast
Questions and Answers
What are two common methods of numerical imputation?
What are two common methods of numerical imputation?
Assigning zero or using the median of the column.
How does target transformation improve model fit?
How does target transformation improve model fit?
It adjusts skewed distributions to make residuals closer to a normal distribution.
What is the primary goal of feature engineering?
What is the primary goal of feature engineering?
To extract valuable features and remove irrelevant or noisy ones.
What potential issue can arise from applying feature transformations incorrectly?
What potential issue can arise from applying feature transformations incorrectly?
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What is one reason why outliers may occur in a dataset?
What is one reason why outliers may occur in a dataset?
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Discuss the importance of splitting data into training and testing sets in machine learning.
Discuss the importance of splitting data into training and testing sets in machine learning.
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What effect does normalizing features have on the performance of a kNN classifier?
What effect does normalizing features have on the performance of a kNN classifier?
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How can data leakage affect the evaluation of a machine learning model?
How can data leakage affect the evaluation of a machine learning model?
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In the context of model fitting, what role does hyperparameter tuning play?
In the context of model fitting, what role does hyperparameter tuning play?
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What is an observable trend regarding fraudulent transactions in the provided data analysis?
What is an observable trend regarding fraudulent transactions in the provided data analysis?
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Study Notes
Feature Engineering Cycle
- Powerful feature transformations can introduce leakage if applied incorrectly
- Requires domain knowledge about feature interactions
- Time-consuming and involves numerous experiments
Why Feature Engineering Matters
- Extracts new features and removes irrelevant or noisy features
- Results in simpler models with better performance
Key Elements of Feature Engineering
-
Target Transformation: Modifies predictor/response variable to improve model fit
- Improves model fit when variable shows a skewed distribution
- Transforms data towards a normal distribution
- Examples: log(x), log(x+1), sqrt(x), sqrt(x+1)
-
Feature Extraction: Derives new features from existing ones
- Imputation
- Outlier Detection
- Log Transformation
- Grouping
- Splitting
- Scaling
-
Feature Encoding: Transforms categorical features into numeric features
- Provides more fine-grained information
- Captures non-linear relationships and interactions between feature values
- Essential for machine learning algorithms that require numeric input
- Methods: Labeled Encoding, One Hot Encoding
Imputation
- Addresses the common problem of missing values in data
- Reasons for missing values: human errors, interruptions, privacy concerns
- Solutions:
- Drop the row/column: Simple but can lead to data loss
-
Imputation: Preferred option for filling missing values
- Numerical Imputation: Assigning zero, NA, default values, or medians
- Categorical Imputation: Using the most frequent value or creating an "Other" category
Outlier Detection
- Outliers deviate significantly from the normal data distribution
- Distinguish outliers from noise data:
- Noise is random error or variance
- Noise should be removed before outlier detection
- Outliers are interesting as they violate the typical data generation mechanism
- Potential applications: fraud detection, customer segmentation, medical analysis
Types of Outliers
- Global Outlier (or Point Anomaly): Deviates significantly from the entire dataset
- Contextual Outlier (or Conditional Outlier): Deviates significantly based on a specific context
- Collective Outliers: A subset of data objects collectively deviate significantly, even if individual objects may not be outliers
Finding Outliers
- Methods for outlier discovery:
- Visualization Tools: Box plots, Scatter plots
- Statistical methodologies: Z-score, IQR score
- Advanced techniques for anomaly detection are discussed later
Box Plot
- Graphical method to display groups of numerical data using quartiles
- Extends lines (whiskers) from the boxes to indicate variability outside quartiles
- Outliers are plotted as individual points outside the box
Scatter Plot
- Displays relationships between two variables using Cartesian coordinates
- Plots data points based on values of two variables on the horizontal and vertical axes
Log Transformation
- Used to handle skewed data
- Transforms data to approximate a normal distribution
- Reduces the effect of outliers to make the model more robust
- Data must have only positive values for log transformation
Grouping
- Aggregating data into meaningful groups
- Applies to datasets with different levels of granularity (e.g., transactions)
- Key point is defining aggregation functions for different features
- Aggregating categorical columns:
- Highest frequency: Maximum operation
- Pivot table: Merging features into aggregated, more informative features
- Numerical columns are usually grouped using sum and mean
Splitting
- Processes string columns that violate tidy data principles
- Splits features based on specific criteria (e.g., words in text)
Scaling
- Addresses the issue of numerical features with different ranges
- Essential for algorithms that work based on distance (e.g., k-NN, k-Means)
- Methods:
- Normalization
- Standardization
Normalization (Min-Max normalization)
- Scales values to a fixed range between 0 and 1
- Does not change the distribution of the feature
- Increases the effect of outliers due to decreased standard deviations
Standardization (Z-Score normalization)
- Scales values while considering standard deviation
- Reduces the effect of outliers by normalizing standard deviations
- Ensures features with different standard deviations have similar ranges
Feature Encoding
- Converts categorical features into numeric features
- Enhances the quality and interpretability of categorical information
- Provides more fine-grained information for machine learning models
- Methods:
- Labeled Encoding: Assigns ordered integers to categories
- One Hot Encoding: Creates binary features representing each category
One Hot Encoding
- Creates multiple flag columns representing categories
- Assigns 0 or 1 values to indicate the presence or absence of a category
- Useful for algorithms like K-means, Linear Regression, Neural Networks
Data Visualization & Feature Engineering
- Fraudulent transactions are typically of much higher value than non-fraudulent transactions.
- Non-fraudulent transactions are typically closer to home, but with several outliers.
Data Normalization & Model Training
- Splitting data into training and testing sets is crucial for machine learning models.
- Training data teaches the model to learn relationships between features and target variables.
- Test data evaluates the model's performance by comparing predictions to actual values.
- Normalizing features is important for kNN algorithms as they measure distances between data points.
- The code uses five splits, meaning the data is divided into five equal groups. Four groups are used for training and one for testing.
- Accuracy scores are calculated for each split, averaged to find the best model.
Hyperparameter Tuning & Evaluation Metrics
- The k value (number of nearest neighbors) is a key hyperparameter.
- The code calculates accuracy scores for a range of k values from 1 to 30.
- The results show that k values between 9 and 13 have an accuracy score of around 95%.
- Choosing a smaller k value is generally advisable as it relies on closer data points.
- Other evaluation metrics can be considered, such as precision, recall, and F1-score.
Introduction to kNN Classification
- kNN is a voting system where the majority class label among the nearest k neighbors determines the class label of a new data point.
- The number of nearest neighbors (k) is a crucial factor in determining the model's accuracy and robustness.
- kNN can be used for both classification and regression tasks.
- Distance metrics used for determining proximity between points are important for kNN performance.
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Description
This quiz explores the essential concepts of the feature engineering cycle, focusing on how powerful transformations can both improve model performance and introduce risks if misapplied. Gain insights into target transformation, feature extraction, and feature encoding, which are crucial for building effective machine learning models.
